Tracking is one of the most crucial components of reconstruction in the collider experiments. It is known for high consumption of computing resources, and various innovations have been being introduced until now. Future colliders such as the High-Luminosity Large Hadron Collider (HL-LHC) will face further enormously increasing demand of the computing resources. Usage of cutting-edge artificial intelligence will likely be the baseline at the HL-LHC, but the rapid development of quantum algorithms and hardware could bring in further paradigm-shifting improvement to this challenge. Recent progress in such quantum approaches will be presented in this seminar.
About the speaker:
大川(Okawa)英希(Hideki)，Professor at EPD, IHEP. He obtained his bachelor and PhD at the University of Tokyo in 2004 and 2010 respectively. In 2010-2014, he worked as a postdoc at University of California, Irvine and then at Brookhaven National Laboratory in the USA. In 2014, he joined University of Tsukuba in Japan as an international tenure-track assistant professor and was tenured in 2018. In 2019, he joined Fudan University as a University Key Position Professor through a national young talent program. He joined IHEP in 2022. He is actively applying classical and quantum machine learning techniques to collider experiments.
Zoom link: https://zoom.us/j/91762223690?pwd=SldVcTVZdllKdWgxMTdicCtSeURBdz09
Zoom ID: 91762223690
Zoom password: 328248
EPD seminar，IHEP